This study investigates the effects of post-fabrication annealing on polymer-based capacitive micromachined ultrasonic transducers (polyCMUTs). These devices comprise microscopic diaphragms produced via photolithographic patterning of polymer layers. Critical point drying, required to release the diaphragms, can cause significant plastic deformation, thereby reducing electromechanical coupling. Post-fabrication annealing, carried out in incremental steps up to 190 °C, led to an effective increase in coupling by a factor of 5.4. Atomic Force Microscopy showed that the initial upward deflection of 162.7 nm decreased to 6.2 nm after annealing at 190 °C, while also improving surface uniformity. In parallel, the transducer’s resonance frequency rose from 2.33 MHz (unannealed) to 2.60 MHz, and the input impedance phase angle at resonance increased from −68.1° to −4.3°. Together, these changes indicate a significant improvement in resonator behavior and, consequently, device performance. Thus, post-fabrication annealing is an effective measure to achieve the designed performance while enhancing manufacturing yield, thereby increasing the applicability of polyCMUTs.

A compact single asymmetric coplanar waveguide feed (ACPW‐fed) dual circularly polarized microstrip antenna that operates at 1.8, 3.9, and 5.2 GHz in the entire operating frequency band 600 MHz–6 GHz for the radar detection of the improvised explosive devices (IEDs) carried by a person is introduced. The proposed novel quasi‐omnidirectional antenna consists of single sided rectangular ring microstrip patch antenna. L‐shaped slots are etched at the two opposite corners of the rectangular ring, introducing new resonance and circular polarization waves at the mid and upper bands, respectively. The achieved dual half‐rectangular ring patch antenna (DHRR‐patch) is loaded with strips of various shapes delicately placed at the center of the radiator, providing new resonance at the upper band and the improvement of the CP features. The matching technique designed based on CPW 50 Ω microstrip transmission line combined with the dual broad band matching techniques through quarter‐wave transformer in conjunction with open stubs and distributed lumped element method constitutes the novelty of the study. Based on quasi‐TEM mn (q‐TEM mn ) mode, ACPW‐fed and CP‐slots are employed to generate CP radiations at the q‐TEM 11 and q‐TEM 21 modes, respectively, while the ground plane width is optimized to enhance axial ratio bandwidth (AR‐BW). Input impedance and radiation pattern calculations of the conventional structure using transmission line and cavity model‐based q‐TEM 01 mode are conducted, respectively. Numerical experiments of the studied monolayer antenna are carried out using Advanced Design System (ADS) Version 2009 environment software employing internal one‐port option to excite the antenna. The prototype of the proposed antenna with a compact dimension (0.27 λ g × 0.38 λ g × 0.02 λ g at 1.8 GHz where λ g is the guided wavelength of the q‐TEM 01 mode) is fabricated on high loss laminate FR4 substrate of volume 43 × 38 × 1.6 (mm 3 ) and relative dielectric constant of 4.4 with simple laboratory‐based traditional printed circuit board (PCB) etching process. Measurement results show a fractional impedance bandwidth (FIBW) of 11.1%, 5.9%, and 7.1%, axial ratio (AR) of 4.6, 2.2, and 0.5 dB, and peak gain of 3.7, 4.7, and 6.1 dBic at 1.8, 4.0, and 5.2 GHz, respectively, demonstrating its suitability for IED detection applications. To verify the efficiency of the proposed model, measured results are compared with the simulated results and good agreement has been established.

A chopper capacitively coupled instrumentation amplifier with a fast-detecting DC-servo loop and an improved input impedance boosting technique

Wireless power transfer is gaining attention in medium-to-short-range applications such as 1–3 kW-class UAVs and AGVs due to its safety, reliability, and adaptability to complex environments. The LCC-S topology is widely adopted due to its favorable output characteristics and device voltage-stress distribution. However, under fixed coil parameters and operating frequencies, conventional LCC-S achieves high efficiency only near the optimal equivalent load. When the actual load deviates from this value—especially in heavy-load regions—resonant cavity current increases sharply, voltage gain drops significantly, and overall efficiency deteriorates. To overcome this structural limitation without increasing control complexity or adding active regulation stages, this paper proposes a transformer-assisted LCC-S wireless charging topology based on “equivalent load reconstruction.” First, a unified equivalent circuit is constructed to derive analytical expressions for voltage gain, input impedance, and efficiency under arbitrary coupling coefficients and loads for both the traditional LCC-S and the proposed topology, revealing the mechanism behind efficiency degradation under heavy loads. Building upon this foundation, a high-frequency transformer is introduced, with an efficiency-oriented collaborative design method for its turns ratio and excitation inductance. Furthermore, by integrating simplified copper and iron-loss models, the losses in the resonant cavity and the transformer are decomposed and evaluated. Results demonstrate that when transformer parameters are appropriately selected, the newly introduced transformer losses are significantly smaller than the resonant cavity losses reduced through load reconstruction. The constructed 1 kW, 85 kHz prototype demonstrates that within the 0.5–2.5 Ω load range, the proposed topology achieves efficiency exceeding 88%. Under typical heavy-load conditions, its peak efficiency surpasses that of the conventional LCC-S by approximately 20%. The theoretical analysis, simulation, and experimental results are highly consistent, verifying that the transformer-assisted LCC-S topology and its efficiency-oriented design method can effectively expand the high-efficiency operating range across a wide load spectrum without altering the control strategy. This provides a concise and feasible structural optimization solution for wireless charging systems.

In this work, a layout-level design methodology is presented for Low-Noise Amplifiers (LNAs), targeting a wide frequency spectrum from RF to millimeter-wave (mmWave) bands, and implemented using a 22 nmFDSOI CMOS process. A nested inductor structure is introduced at RF frequencies to reduce silicon footprint, with magnetic crosstalk effects characterized through electromagnetic (EM) simulations using Ansys® RaptorX, Release 2024 R2, ANSYS, Inc. and integrated into the design process. Single-ended LNA architectures are employed for RF bands, while at mmWave frequencies, a differential topology is adopted to enhance linearity and enable simultaneous input and output impedance matching. An EM-based verification flow is applied across all designs to ensure RF/mmWave design flow compatibility, simulation accuracy, and enhanced performance. The proposed designs are evaluated using key metrics including input/output matching, reverse isolation, forward gain, noise figure, linearity (IP1,IP3), stability factor, power consumption, and total chip area to quantify the efficiency of the proposed methodology. The simulation results demonstrate that nested inductors are highly effective for area reduction in RF LNAs, while differential topologies are more suitable for mmWave designs, providing a unified framework for area-efficient and high performance LNA implementation.

A behind-the-ear (BTE) integrated interface for mental healthcare applications is presented, featuring optimized BTE electrode configurations and wide multimodal biomedical IC with adaptive compensation capabilities. The proposed IC supports 8 bio-potential (ExG), 1 photoplethysmogram (PPG), 1 galvanic skin response (GSR), 1 bio-impedance (BioZ), and 2 stimulation channels. The ExG channel achieves 2.5GΩ input impedance, boosted by 308 times with offset compensated auxiliary path (OCAP) architecture, and its AC input impedancecharacteristic is boosted further by dual resolution external positive feedback loop (DR-EPFL) scheme. An area and energy-efficient GSR-embedded ECG recording scheme is presented. For comprehensive multimodal sensing features, dual-slope PPG channel with parasitic capacitance compensation, electrode-tissue impedance adaptive stimulator, and high dynamic range BioZ channel are integrated. The IC was fabricated in a 0.18-μm BCD process and integrated into a BTE patch-type device prototype. System-level feasibility was experimentally verified through in-vivo stress measurements with virtual reality (VR) environment, demonstrating effective mental health monitoring capabilities.

Microstrip patch antennas are vital for Ka-band communication owing to their compact size and high performance. This study introduces a modified patch design at 28 GHz featuring two corner truncations and dual-slot integration to enhance impedance matching and broaden the operational bandwidth. The objective of this work is to investigate whether geometrical modifications combined with intelligent modelling can yield improved performance metrics while accelerating the performance evaluation phase through a data-driven surrogate model. The proposed antenna was developed through parametric optimization in Ansys HFSS, in which its structure was systematically varied to achieve stable resonance and improved radiation performance. The optimized prototype achieves a simulated return loss of −67.11 dB, a bandwidth of 3.8 GHz, a VSWR of 1.0009, a peak gain of 7.65 dB, and an input impedance of 50.01 Ω, all indicating strong simulated electromagnetic performance. The design demonstrates a deep resonance corresponding to a high quality (Q) factor, making it a suitable candidate for applications where precise frequency selectivity is paramount. To accelerate evaluation, a machine learning framework was integrated, using 65,682 simulated samples to train regression models for predicting return loss. Among the tested algorithms, the Random Forest Regressor demonstrated the highest accuracy with a mean absolute error of 0.0471 dB and an R² of 0.9995. The integration of electromagnetic simulation and ML-assisted performance prediction demonstrates a reliable pathway for rapid evaluation of Ka-band antennas, offering strong potential for next-generation satellite and wireless communication systems.

This article presents a direct-digitization analog front end (DD-AFE) with enhanced input-impedance, common-mode rejection ratio (CMRR), and dynamic range (DR) for wearable biopotential (ExG) signal acquisition, especially for small-diameter dry electrodes. The DD-AFE employs a second-order continuous-time delta-sigma modulator (CT-ΔΣM) and multiple circuit techniques to support direct-digitization readouts. These include 1) A high input-impedance input feedforward (FF), embedded in a 4-input 4-bit successive approximation register (SAR) quantizer. This allows two integrators to adopt a compact and energy-efficient Gm-C structure, and improves stability and linearity, resulting in a 6.6dB increase in DR, 42dB increase in SQNR at peak input and a unity-gain signal transfer function (STF) with a gain flatness of 0.04%. 2) A fixed-voltage dead-band assisted tri-level current-steering DAC (IDAC). It not only increases the DR and CMRR of the DD-AFE but also eliminates the harmonic distortion induced by tri-level dynamic element matching (DEM). 3) A high-gain two-stage Gm-boosting inverter-based OTA with embedded low-frequency chopping. The former largely improves linearity and CMRR, while the latter mitigates 1/f noise without compromising the input impedance. Fabricated in a 0.18-µm CMOS process, this DD-AFE achieves 6.4GΩ input impedance and 104.5dB CMRR at 50Hz, as well as 90.4dB peak SNDR, 96dB DR, and up to 425mVPP linear input range.

Long-term, continuous health monitoring imposes stringent demands on bio-recording analog front-end (AFE) circuits, specifically in terms of dynamic range (DR), noise, input impedance, and power consumption. This work introduces a DR-enhanced direct-digitization AFE based on a Δ-modulated trans-conductor (TC) stage, followed by a second-order ΔΣ ADC. In this architecture, the accumulated DAC is subtracted exclusively at the TC input stage, allowing the integrators to process only the low-amplitude Δ-modulated signal and thus relaxing the dynamic range constraints of conventional Gm-C ΔΣ ADCs. The TC input stage achieves high input impedance and high linearity through a current-balancing transconductor and a flipped-voltage-follower (FVF) loop. Fabricated with a standard 180nm CMOS process, the proposed Δ-ΔΣ AFE exhibits an SNDR of 91 dB, a dynamic range of 101 dB, input referred noise of 58 nV/$\surd{\rm Hz}$, and a power consumption of 63 $\boldsymbol{\mu}$W. These results correspond to a FoMSNDR of 160.1 dB and a FoMDR of 170 dB. The AFE prototype has been validated through scalp EEG, leg EMG, and chest ECG with significant body movements, demonstrating its effectiveness as a motion-artifact-tolerant direct-ADC front end.

Ultrasonic pulse transmission through an acoustic barrier using a metamaterial with input impedance matching

Abstract Bionic limbs require reliable, low-noise and high-comfort interfaces between electrodes and the prosthetic system. This work presents the first fully flexible, wearable myoelectric control system compatible with both dry and wet electrodes. It features a low-noise front-end circuit on foil using amorphous Indium-Gallium-Zinc-Oxide (a-IGZO) Thin-Film Transistors, optimized for multi-electrode sensing. The design includes an autozeroed pre-charging buffer to minimize offset and 1/f noise while maintaining high input impedance (841 MΩ at 50 Hz). The front-end achieves 22 µVrms input noise, < −90 dBc crosstalk, and a 4.6 mV input offset consuming 55.3 µW per channel. EMG signals measured by this AFE were used to drive an elbow musculoskeletal model and predict the resulting human elbow flexion-extension moments, which in turn were used to realize a closed-loop real-time control in a simulated bionic elbow joint, using both dry and wet electrodes. Experiments done with a series of movements show a 20°rms error in angular control.

Large-surface-area carbon materials have been studied widely as solid-contact (SC) materials for ion-selective electrodes (ISEs), as their high nonfaradaic capacitance has been associated with high potential stability. However, recent work highlighted that the very slow potential discharge of single-walled carbon nanotube (SWCNT) solid-contact interfaces as a result of slow, unexpected redox processes of the SWCNTs causes potential drift, which, albeit of very low magnitude, limits the long-term stability of SC-ISEs. Successive use of chronopotentiometry (CP), chronoamperometry (CA), and long-term open-circuit potential measurements (Pot) in a CP-CA-CP-Pot-CP sequence provides the temporal resolution to distinguish between such redox reactions of the high-surface-area carbon and the charge redistribution artifacts that may bias capacitance measurements by chronopotentiometry. We discuss here differences in the charge redistribution and redox processes of solid contacts made of nanographite, mesoporous carbon nanospheres (MCN), and SWCNTs, as studied with the CP-CA-CP-Pot-CP technique. After brief applications of small voltages to mimic effects of the finite input impedance of real-life potentiometers, MCN and nanographite interfaces exhibited no changes in capacitances, as they were previously observed for SWCNT interfaces. However, contact angle measurements after application of a small voltage over 1 day suggest that SWCNTs, MCN, and nanographite all undergo surface oxidation to some extent, nanographite being most sensitive to oxygen. These results demonstrate that discharge mechanisms vary across different carbon materials and that a large capacitance cannot guarantee greater electrode stability unless redox reactions are effectively suppressed. The development of high-surface-area solid contacts with minimal redox reactivity will be critical for the further improvement of SC-ISEs with high long-term stability and calibration-free measurements.

Computational models, particularly finite-element (FE) models, are essential for interpreting experimental data and predicting system behavior, especially when direct measurements are limited. Tuning these models is particularly challenging when a large number of parameters are involved. Traditional methods, such as sensitivity analyses, are time-consuming and often provide only a single set of parameter values, focusing on reproducing averaged trends rather than capturing experimental variability. New approaches are needed to make computational models more adaptable to patient-specific clinical applications. We applied simulation-based inference (SBI) using neural posterior estimation (NPE) to tune an FE model of the human middle ear against subject-specific data. The training dataset consisted of 10,000 FE simulations of stapes velocity, ear-canal input impedance, and absorbance, paired with seven FE parameter values sampled within plausible ranges. By using simulated data, we generated a diverse training dataset, enabling efficient learning by the neural network (NN). The NN learned the association between parameters and simulation outcomes, providing a probability distribution of parameter values, which could be used to produce subject-specific computational inferences. By accounting for noise and test–retest variability, the method provided a probability distribution of parameters, rather than a single set, fitting three experimental datasets simultaneously. Importantly, examining the inferred parameter distributions alongside prior knowledge of normal ranges enables individualized differential inference used for diagnosis. SBI offers an objective alternative to sensitivity analyses, uncovering parameter interactions, supporting personalized diagnosis and treatment, and compensating for limited clinical training data. This method is applicable to any computational model, enhancing its potential for improved patient outcomes.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-22164-2.

Surface-mounted devices (SMDs) are essential components that enable the miniaturization and enhanced performance in electronic products, significantly impacting both circuit performance and reliability. In this study, we propose a non-destructive evaluation method for cracks in SMD capacitors using the artificial intelligence of impedance spectra. To achieve this, cracks were induced in 132 specimens through incremental displacement using a shear module of a bond tester. At each crack level, frequency-domain spectra were acquired for 14 parameters using an impedance analyzer. Meaningful changes in parameter patterns corresponding to each crack stage were observed, confirming impedance spectroscopy as an effective tool for crack assessment. Through data augmentation, we generated 87,800 datasets representing various crack stages, which were used to train AI models that output crack stages from input impedance spectra. Based on this dataset, six AI models, ConvNeXt, LSTM, Transformer, Logistic Regression, SVM, and Random Forest, were developed to classify crack severity into nine stages. Model-wise, the Random Forest classifier consistently outperformed the other approaches. When trained with single parameters, it achieved its best performance using the dissipation factor, reaching 98.5% accuracy. Furthermore, when the dissipation factor was combined with any of the remaining impedance parameters, the Random Forest model achieved perfect diagnostic performance (100%) across all combinations, highlighting both its robustness and its suitability for multi-parameter learning. These results provide practical guidance for selecting effective parameters and model architectures for impedance spectrum-based crack diagnostics.

Abstract High-performance cryogenic amplifiers are essential components for quantum information technology and astronomical observation. In this work, we report the design and characterization of a microwave amplifier based on superconducting quantum interference device (SQUID). The design incorporates input and output impedance transformers to center the operating frequency at approximately 5.5 GHz for specific applications. Preliminary characterization at a bath temperature of 4.2 K demonstrates a peak gain of 10.79 dB at 5.39 GHz and a saturation power of approximately -115 dBm.

In radio frequency (RF) systems, the mixer is a critical component for achieving frequency conversion in electromagnetic sensor backends. This paper proposes a mixer design methodology aimed at improving noise figure and conversion gain specifically for sensor signal processing applications. This design employs a process incorporating high-quality bipolar junction transistors (BJTs) and adopts a mixer-first architecture instead of a conventional low noise amplifier (LNA). By optimizing the layout and symmetry of the BJTs, the input impedance can be flexibly adjusted, thereby simplifying the receiver front-end while simultaneously improving local oscillator (LO) feedthrough. Design and simulation were completed using Advanced Design System (ADS) 2020 software. Simulation results demonstrate that the proposed mixer exhibits significant advantages in suppressing noise and interference while enhancing conversion gain, making it particularly suitable for electromagnetic sensor backend applications.

HighlightsWhat are the main findings?A passive wireless temperature sensor is implemented using SS-compensated magnetically coupled LC tanks.The thermistor’s resistance is directly reflected in the input impedance at split resonance frequencies.What is the implication of the main finding?Enables accurate, battery-free, and contactless temperature sensing through impedance monitoring.Suitable for applications requiring simple and reliable wireless sensing topologies.This paper presents a passive wireless temperature sensor based on an SS-compensated LC-thermistor topology. The system consists of two magnetically coupled LC tanks—each composed of a coil and a series capacitor—forming a series–series (SS) compensation network. The secondary side includes a negative temperature coefficient (NTC) thermistor connected in series with its coil and capacitor, acting as a temperature-dependent load. Magnetically coupled resonant systems exhibit different coupling regimes: weak, critical, and strong. When operating in the strongly coupled regime, the original resonance splits into two distinct frequencies—a phenomenon known as bifurcation. At these split resonance frequencies, the load impedance on the secondary side is reflected as pure resistance at the primary side. In the SS topology, this reflected resistance is equal to the thermistor resistance, enabling precise wireless sensing. The advantage of the SS-compensated configuration lies in its ability to map changes in the thermistor’s resistance directly to the input impedance seen by the reader circuit. As a result, the sensor can wirelessly monitor temperature variations by simply tracking the input impedance at split resonance points. We experimentally validate this property on a benchtop prototype using a one-port VNA measurement, demonstrating that the input resistance at both split frequencies closely matches the expected thermistor resistance, with the observed agreement influenced by the parasitic effects of RF components within the tested temperature range. We also demonstrate that using the average readout provides first-order immunity to small capacitor drift, yielding stable readings.

A fractional order output feedback controller for a lung ventilator is designed. This is based on a state-of-the-art electrical analogue model of the human respiratory system in the form of a network of resistors and fractional capacitors. The electrical input impedance of the adopted analogue can be suitably tuned to fit experimental ventilation impedance data. Furthermore, it can explicitly account for the different physiological fractal type characteristics associated with lung formation such as branching morphogenesis associated to the treelike tubular network and alveolar differentiation associated with the generation of specialized epithelial cells for gas exchange. A description of this electrical analogue in pseudo-state space is then proposed. The aim is to finally provide a control methodology within the scope of output feedback control, when the measured output which is the airflow through the trachea is directed to follow a specified reference. The control provides adequate air pressure input to generate this nominal airflow. The proposed control design includes a pseudo-state observer and a double leaky integrator. The gains involved are designed using constraints imposed through linear matrix inequalities (LMIs), which enforce a regional allocation of eigenvalues. The robustness of the control loop is analysed through an uncertainty matrix analysis linked directly to the model. It is observed that the proposed design can tolerate a relatively wide variation in physiological parameters (pm 15%). The proposed formulation advances current control design approaches for mechanical ventilators and provides a generic methodology for the control of complex system with emergent responses as encountered in bioengineering.

Open-source low-cost non-contact ECG monitoring system using active dry electrodes

A Rail-to-Rail Input NS-Pipelined-SAR ADC With Self-Boosted Input Impedance and Reused Residue Amplifier for Biosignal Acquisition